Sunday, June 23, 2013

Part I, in which I sliced the data by state, is here. The state-level approach has some nice intuitive appeal, but in general I don't think state lines are always great ways to divide economic activity (but they do have a few advantages since they make their own laws).

This time I'll look at Metropolitan Statistical Areas, or cities (data for these are now available in the newest vintage of the BDS; other city definitions, like commuting zones, are not). The nice thing about MSAs is that they can cross state lines; for example, Washington, DC includes parts of Virginia and Maryland. Philadelphia includes parts of Delaware and New Jersey. MSAs are county based; that is, an MSA is a collection of counties. The BDS uses 2009 definitions, provided here (the definitions can change over time, but for time series work we need constant definitions). I'll also briefly look at one region that does not map to a single MSA, Silicon Valley.

Figure 1

Figure 1 (click for larger image) plots startup rates by MSA (actually, the map template is for counties, so the MSA dividers aren't always ideal--sorry). I have used the average of the 10-year period of 1997-2007, hoping to avoid cyclical issues. Darker means higher startup rates, where the startup rate is the percent of all MSA firms that are startups (new firms). From my post on state-level startup rates, some of this is unsurprising (e.g., high startup rates in Utah and Florida). Note that this is a within-city measure of startup activity; it says nothing about how significant is the city's contribution to national startup activity, but it does say something about the nature of firm dynamics within a city. Figure 2 shows the top 10 cities for internal startup activity (click for larger image):

Figure 2

These are mostly small cities. Interestingly, Florida has five of the top ten (Palm Coast, Miami, Orlando, Cape Coral/Fort Myers, and Naples). Utah has three of the top ten (Provo/Orem, St. George, and Ogden). In all of the top ten, 12 to 15 percent of all firms are startups in a given year (on average for the 10-year period).

In my state-level post, I found that California, Florida, New York, and Texas are the top contributors to national startup activity. Are the top startup cities in those states? Figure 3 shows cities' share of national startups; that is, I sum all startups in the city for 1997-2007 then divide it by all startups for the US for 1997-2007 (click for larger image).

Figure 3

Few cities are significant contributors to national startup activity. Observe that, despite the state's large contribution to startup activity, Texas has no large contributing cities. The big contributors can be clearly seen on the map: New York City, Chicago, Miami, and Los Angeles (Atlanta shows up, but it is just barely above several other cities; I chose the cutoffs poorly). Figure 4 shows the top ten cities for contribution to national totals (click for larger image):

Figure 4

Despite New York only being the #3 state, New York City contributes an astounding 8 percent of startups in the US. This breakdown shows the value of looking at cities instead of states. In my state-level analysis, Texas earned its place high on this list--but here we learn that both California and Texas have lots of cities with moderate contributions (plus one big city for California), whereas New York City actually provides more startup activity than New York State. The chart also shows that Washington DC does its part for startup activity, but this could not show up in state-level analysis (treating DC as a state is always odd). In any case, the utility of examining cities that cross state lines is shown here.

An aside on Silicon Valley: According to this BLS document, Silicon Valley doesn't map to a specific MSA but instead includes counties in the San Francisco, San Jose, and Santa Cruz MSAs. Unfortunately, the BDS does not provide county-level data. An upper bound can be obtained by looking at all three MSAs as a group. Using this definition, I found that Silicon Valley accounts for 2.45 percent of national startups, placing it below Miami (2.69 percent) and above Atlanta (2.01 percent). That's a respectable quantity, but it may be smaller than some would expect. Of course, not all startups are created equal, and it's possible that growth-weighted data would look different.

Finally, as with my state analysis, my city analysis shows that some areas are increasing their startup activity faster than others. Figure 5 shows the change in share of national startup activity by MSA from the average of the 1987-89 period to the 2004-06 period (click for larger image):

Figure 5

Florida consistently shows itself to be a hotbed of startup activity with positive growth in its share, increasing its share of national activity by more than 1.5 percentage points. Many cities show positive growth, which is not surprising given the shift of economic activity from rural to urban areas. Figure 6 shows the share of startup activity accounted for by metropolitan areas (as opposed to non-metropolitan areas) since 1978 (click for larger image):

Figure 6

Metro areas accounted for around 80 percent of startups in 1980 but now account for more than 86 percent. This trend shows no signs of abating.

I think the above analysis shows a few things.

First, common perceptions about the concentration of startup activity may be flawed; I wonder who among startup enthusiasts would have predicted that Miami and Chicago produce more startups than Silicon Valley. But this is a very general definition of startup that includes everything from the new local dentist to the next Google; things may be different if we use a more fashionable definition.

Second, there is a difference between having startup activity that is quantitatively significant in national terms and having startup activity that is quantitatively significant in local terms. Both tell us something about cities. Some places are both highly dynamic in a local sense and important for national activity (e.g., Florida), while others may lack national significance but clearly have strong startup activity as a share of the local economy (e.g., several Utah cities). Still other regions contribute much to national activity by nature of the fact that they are large economies, but startups are not a large share of local activity.

Third, the way we slice geography matters; some of the results from this analysis of metropolitan areas are surprising in light of my previous analysis of states, and I suspect that dividing cities up in different ways (e.g., commuting zones) could provide other surprises as well. There are also strong implications of industry trends for this kind of analysis, since industry and geography are closely linked. The broader point is that the way we aggregate matters.

Finally, it's important to keep in mind that this regional activity is occurring against the backdrop of a secular decline in startup activity (see charts here). Further, I have not here examined the job market implications of this; we know that startups basically account for all net job creation, and I haven't here looked at how that fact interacts with geography.

I'm not sure what data we're looking at here, but I assume its definition of Available Labor Supply is something like the CPS workforce definition. Aziz's argument makes a lot of sense in a representative agent or homogenous agent world. If there are 100 job applicants, and there are 50 job openings, then obviously none of the unemployment is voluntary because the job market wouldn't absorb any new job applicants even if they tried. This depends a bit on semantics, but it's good enough for me, to a first approximation.

But consider a model with two types of skillsets, carpentry and blogging. Suppose there are 50 people who count as unemployed (they are "seeking work"): 25 carpenters and 25 bloggers. Now suppose there are 45 job openings. By Aziz's logic, there is no voluntary unemployment. Now I tell you that 25 of the job openings are for carpenters, but only 20 of the carpenters are applying for those jobs while the other 5 carpenters are just "talking to friends about job opportunities" so they count as unemployed people. So it's possible that 5 carpenters are voluntarily unemployed (in anything but a semantic debate). And this argument can extend to geographic heterogeneity or anything else; it's pretty hard to pin down how much it matters.

Is this kind of mismatch happening now? I don't know, but if there is evidence that reservation wages are high or job-search intensity is low, I don't think we can rule it out. If you acknowledge heterogeneity and admit that transfers affect incentives to search for jobs (or start businesses), then I don't think you can be as confident as Aziz. We at least need more evidence about mismatch and other things.

The broader point is that too much aggregation can get you into trouble with some questions. Simply knowing aggregate numbers for job seeking and openings doesn't tell us all we need, since we don't know how many potential openings exist for every kind of worker. Further, we should keep in mind possible general equilibrium effects; what would happen to wages or entrpereneurship if people had a smaller safety net, and how would that affect labor demand? Again, this chart doesn't tell us.

I'm certainly not suggesting that transfers are the dominant driver of the current employment situation, but I think Aziz goes too far in suggesting that there's no involuntary unemployment out there.

Monday, June 17, 2013

Given the cross-industry heterogeneity in startup activity, it should not be a surprise that some regions see more startups than others. The importance of geography for economic activity does not seem to be going away, and startups are really important, so it's useful to look at state differences in new firm formation (I'll try to look at metropolitan areas later). As always, I define a startup as a new firm. Figure 1 shows startup rates by state, where the startup rate is the ratio of startups to total firms within a state (click for larger image).

Figure 1

Here I have taken the 10-year average, roughly peak-to-peak on the business cycle. As expected, there is a pretty wide variety ranging from DC at 3.5 percent to Nevada at 7.3 percent (for those who don't like seeing DC treated as a state, North Dakota is next-lowest at 3.8 percent). Next, Figure 2 shows state startup shares--startup firms by state as a percent of the national total (click for larger image).

Figure 2

Again, I have used the 10-year average. Most states produce few startups. Figure 3 shows the top ten states (click for larger image):

Figure 3

Note that the top ten states account for more than half of economywide startup formation, with California alone providing more than 12 percent. But this is a snapshot in time, and things are on the move. Figure 4 considers the change in state shares of national startup activity. Here I have used the difference between three-year averages (1987-89 and 2004-06), capturing the business cycle peak-to-peak change (click for larger image).

Figure 4

Florida has increased its share of national startup activity by nearly 2 percentage points, while California has slightly declined. The "rust belt" and the northeast have declined as well, with many western states and Texas gaining. I haven't looked at whether these trends held up through the Great Recession.

Keeping in mind that geography can be tricky in the internet age, startup activity may be undergoing a major regional transition. It is likely driven in part by industry trends. Data can be noisy, and I have not here considered job creation quantities from startups, but these facts may be worth considering in the context of other discussions of geography and economics.

Friday, June 7, 2013

On May 10, 2010, the ECB announced that it would buy peripheral sovereign debt, ostensibly to "restore an appropriate monetary policy transmission mechanism" but also to address the debt crises in Greece, Portugal, and Ireland. Axel Weber (then-president of the Bundesbank and, therefore, member of the ECB Governing Council) was strongly opposed to the program. The following occurred prior to the announcement but after the decision.

Shortly after the Governing Council meeting Sunday evening, Weber convened a conference call of the Bundesbank Executive Board. . . . Officially he wasn't supposed to tell anyone of what the Governing Council had just decided, but this was so momentous that he posed a quite serious question to the board members: Should we do it? Should the Bundesbank follow its marching orders from the ECB and buy billions of euros' worth of Greek and Portugese bonds, violating its long-cherished principle of not using the printing press to fund governments? . . .

Staring at that precipice, the Bundesbank concluded it was better to hold its nose and violate orthodoxy than to unleash such dangerous consequences.

This jarring revelation is from page 231 of Neil Irwin's book, The Alchemists, which I have very much enjoyed.